Supervisor
Dr. Muhammad Iqbal
Programme
BSc (Hons) in Computing in IT
Abstract
The project presents a deep learning solution to classify brain tumors through MRI images. Following the CRISP-DM framework, two Convolutional Neural Network (CNN) models were developed and evaluated, a custom CNN designed from scratch and a pretrained ResNet50 that was transfer learned and fine-tuned. Both models were assessed using standard performance metrics such as accuracy, precision, recall and F1-score. Despite the higher test accuracy achieved by the custom CNN, further interpretability indicated inconsistent attention to the actual tumor regions also known as shortcut learning. On the other hand, ResNet50 showed more reliable and clinically relevant focus which supported its selection as the final model. The selected model was deployed using Gradio, to demonstrate a real-world application with real-time predictions and visual explanation to reduce the black box nature of AI. The results showcase that despite the good performance of custom and tailored CNN models on specific datasets, generalization and real-world relevance are equally important for reliable deployment
Date of Award
2025
Full Publication Date
2025
Access Rights
open access
Document Type
Undergraduate Project
Resource Type
bachelor thesis
Recommended Citation
Bentemessek, R.
(2025) Brain Tumor Classification using Deep Learning: Custom CNN vs. ResNet50 CCT College Dublin.
DOI: https://doi.org/10.63227/652.199.42